QLIO：量化激光雷达-惯性里程计
QLIO: Quantized LiDAR-Inertial Odometry
AI总结：
QLIO提出多处理器分布式量化LIO框架，通过协处理器压缩残差和rQ向量自适应重采样，在保持定位精度的同时降低计算负载与带宽消耗，实测减少14.1%残差数据量。
摘要：
激光雷达-惯性里程计（LIO）被广泛用于自主导航，但其在尺寸、重量和功耗（SWaP）受限平台上的部署仍然具有挑战性，原因在于处理稠密点云的计算成本较高。传统LIO框架依赖单一机载处理器，导致计算瓶颈和高内存需求，使得在嵌入式系统上难以实现实时运行。为解决这一问题，我们提出QLIO，一种多处理器分布式量化LIO框架，在保持定位精度的同时降低计算负载和带宽消耗。QLIO引入了一条量化状态估计流水线，其中协处理器对激光雷达测量进行预处理，压缩点到平面残差，仅将必要特征传输给主处理器。此外，一种基于rQ向量的自适应重采样策略智能地选择并压缩关键观测，进一步减少计算冗余。真实世界评估表明，QLIO在保持定位精度的同时，将逐观测残差数据量减少了14.1%。此外，我们发布了开源实现，以促进进一步研究和实际部署。这些结果使QLIO成为在计算和带宽约束下运行的实时自主系统的高效且可扩展的解决方案。
英文摘要：
LiDAR-Inertial Odometry (LIO) is widely used for autonomous navigation, but its deployment on Size, Weight, and Power (SWaP)-constrained platforms remains challenging due to the computational cost of processing dense point clouds. Conventional LIO frameworks rely on a single onboard processor, leading to computational bottlenecks and high memory demands, making real-time execution difficult on embedded systems. To address this, we propose QLIO, a multi-processor distributed quantized LIO framework that reduces computational load and bandwidth consumption while maintaining localization accuracy. QLIO introduces a quantized state estimation pipeline, where a co-processor pre-processes LiDAR measurements, compressing point-to-plane residuals before transmitting only essential features to the host processor. Additionally, an rQ-vector-based adaptive resampling strategy intelligently selects and compresses key observations, further reducing computational redundancy. Real-world evaluations demonstrate that QLIO achieves a 14.1% reduction in per-observation residual data while preserving localization accuracy. Furthermore, we release an open-source implementation to facilitate further research and real-world deployment. These results establish QLIO as an efficient and scalable solution for real-time autonomous systems operating under computational and bandwidth constraints.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
